{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The porepy grid structure"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this tutorial we investigate the PorePy grid structure, and exlain how to access information stored in the grid. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Basic grid construction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The simplest grids are Cartesian. PorePy can create Cartesian grids in 1d, 2d, and 3d. In fact, there are 0d point-grids as well, but these are only used in the context of multiple intersecting fractures. To create a 2d Cartesian grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import porepy as pp\n",
    "\n",
    "nx = np.array([3, 2])\n",
    "g = pp.CartGrid(nx)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The resulting cells will be of unit size, thus the grid covers the domain $[0, 3]\\times [0,2]$. To specify the domain size, we need to pass a second argument"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "phys_dims = np.array([10, 10])\n",
    "g_2 = pp.CartGrid(nx, phys_dims)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The grids currently only have node coordinates, together with topological information that we come back to below. To check the grid size, several attributes are provided"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g.num_cells"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g.num_faces"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g.num_nodes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# And finally dimension\n",
    "g.dim"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The node coordinates are stored as "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.,  1.,  2.,  3.,  0.,  1.,  2.,  3.,  0.,  1.,  2.,  3.],\n",
       "       [ 0.,  0.,  0.,  0.,  1.,  1.,  1.,  1.,  2.,  2.,  2.,  2.],\n",
       "       [ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.]])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g.nodes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  0.        ,   3.33333333,   6.66666667,  10.        ,\n",
       "          0.        ,   3.33333333,   6.66666667,  10.        ,\n",
       "          0.        ,   3.33333333,   6.66666667,  10.        ],\n",
       "       [  0.        ,   0.        ,   0.        ,   0.        ,\n",
       "          5.        ,   5.        ,   5.        ,   5.        ,\n",
       "         10.        ,  10.        ,  10.        ,  10.        ],\n",
       "       [  0.        ,   0.        ,   0.        ,   0.        ,\n",
       "          0.        ,   0.        ,   0.        ,   0.        ,\n",
       "          0.        ,   0.        ,   0.        ,   0.        ]])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g_2.nodes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As expected the second grid covers a larger area.\n",
    "\n",
    "We also see that even though the grids are 2d, the nodes have three coordinates. This is general, all geometric quantities in `porepy` have three dimensions, even if they represent objects that are genuinely lower-dimensional. The reason is that for fractured media, we will often work with grids on fracture surfaces that are embedded in 3d domains, and treating this as special cases throughout the code turned out to be overly cumbersome. Also note that the third dimension was introduced automatically, so the user need not worry about this. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Geometric quantities"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To compute additional geometric quantities, grids come with a method `compute_geometry()`, that will add attributes `cell_centers`, `face_centers` and `face_normals`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.5  1.5  2.5  0.5  1.5  2.5]\n",
      " [ 0.5  0.5  0.5  1.5  1.5  1.5]\n",
      " [ 0.   0.   0.   0.   0.   0. ]]\n"
     ]
    }
   ],
   "source": [
    "g.compute_geometry()\n",
    "print(g.cell_centers)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And similar for face information. It is of course possible to set the geometric quantities manually. Be aware that a subsequent call to `compute_geometry()` will overwrite this information.\n",
    "\n",
    "It is sometimes useful to consider grids with a Cartesian topology, but with perturbed geometry. This is simply achieved by perturbing the nodes, and then (re)-computing geometry:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1.66666667  5.          8.33333333  1.66666667  5.          8.33333333]\n",
      " [ 2.5         2.5         2.5         7.5         7.5         7.5       ]\n",
      " [ 0.          0.          0.          0.          0.          0.        ]]\n",
      "[[ 2.13786506  5.31809719  8.67292596  1.95714395  5.33781635  8.97774084]\n",
      " [ 3.01989956  2.96173701  3.12490568  7.76118978  8.13573849  8.0773526 ]\n",
      " [ 0.49489563  0.51281488  0.78273195  0.31681708  0.34349466  0.62368686]]\n"
     ]
    }
   ],
   "source": [
    "g_2.compute_geometry()\n",
    "print(g_2.cell_centers)\n",
    "g_2.nodes = g_2.nodes + np.random.random((g_2.nodes.shape))\n",
    "g_2.compute_geometry()\n",
    "print(g_2.cell_centers)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When perturbing nodes, make sure to limit the distortion so that the grid topology still is valid; if not, all kinds of problems may arise."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`porepy` provides two ways of visualizing the grid, matplotlib and vtk/paraview. Matplotlib visualization is done by"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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p2rVrF0ELVwSmDgLtVF9frx07dqisrExjx45VeHg4IQsA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\nNsgNmFsarlQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fca31c68f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "pp.plot_grid(g, figsize=(15,12))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we see, the plot is in 3d, and the third axis adds noise to the plot. The matplotlib interface is most useful for quick visualization, e.g. during debuging. For instance, we can add cell numbers to the plot by writing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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i6oldVFa6JUnt27e3OJKml5uba+t+1qxJYuc+VlZWqqSkRAkJ9v7sufC92q9f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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fca363262b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cell_id = np.arange(g.num_cells)\n",
    "pp.plot_grid(g, cell_value=cell_id, info='c', alpha=0.5, figsize=(15,12))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For further information, see the documentation of `plot_grid`.\n",
    "\n",
    "The second visualization option dumps the grid to the vtk (or rather vtu; the grids are considered unstructured):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "e = pp.Exporter(g, 'grid')\n",
    "e.write_vtk()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This file can then be accessed by e.g. paraview. Again, see `export_vtk()` for further documentation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Topological information"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In addition to storing coordinates of cells, faces and nodes, the grid object also keeps track of the relation between them. Specifically, we can access:\n",
    "1. The relation between cells and faces\n",
    "2. The relation between faces and nodes\n",
    "3. The direction of `face_normals`, as in which of the neighboring cells has the normal vector as outwards pointing.\n",
    "\n",
    "Note that there is no notion of edges for 3d grids. These are not usually needed for the type of numerical methods that are primarily of interest in `porepy`. The information can still be recovered from the face-node relations, see comments below.\n",
    "\n",
    "The topological information is stored in two attributes, `cell_faces` and `face_nodes`. The latter has the simplest interpretation, so we start out with that one:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<12x17 sparse matrix of type '<class 'numpy.bool_'>'\n",
       "\twith 34 stored elements in Compressed Sparse Column format>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g.face_nodes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that the information is stored as a scipy.sparse matrix. From the shape of the matrix, we conclude that the rows represent nodes, while the faces are stored in columns. We can get the nodes for the first face by brute force by writing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 4])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.where(g.face_nodes[:, 0].toarray())[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That was hardly elegant, though, and would make for cumbersome implementation of, say, a numerical method. A better approach is to utilize the csc storage format, and write"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 4], dtype=int32)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g.face_nodes.indices[g.face_nodes.indptr[0] : g.face_nodes.indptr[1]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To see why this works, confer the scipy.sparse documentation. Getting the faces of a node can be done by converting `g.face_nodes` to a csr_matrix, and then follow the above procedure.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The map between cells and faces is stored in the same way, thus the faces of cell 0 is found by"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0  1  8 11]\n"
     ]
    }
   ],
   "source": [
    "faces_of_cell_0 = g.cell_faces.indices[g.cell_faces.indptr[0] : g.cell_faces.indptr[1]]\n",
    "print(faces_of_cell_0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "However, `cell_faces` also keeps track of the direction of the normal vector relative to the neighboring cells, by storing data as $\\pm 1$, or zero if there is no connection between the cells (in contrast, `face_nodes` simply consist of '`True` or `False`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1.,  1., -1.,  1.])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g.cell_faces.data[g.cell_faces.indptr[0] : g.cell_faces.indptr[1]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compare this with the face normal vectors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.,  1., -0., -0.],\n",
       "       [-0., -0.,  1.,  1.],\n",
       "       [ 0.,  0., -0., -0.]])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g.face_normals[:, faces_of_cell_0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We observe that positive data corresponds to normal vector pointing out of the cell. This is a very useful feature, since it in effect means that the transpose of `g.cell_faces` is the discrete divergence operator for the grid.\n",
    "\n",
    "As with the face-node relations, we can obtain the cells of a face by representing the matrix in a sparse row storage format, and then use the above procedure of indices and index pointers. However, we know that there will be either 1 or 2 cells adjacent to each face. It is thus feasible to create a dense representation of the cell-face relations:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1,  0,  1,  2, -1,  3,  4,  5, -1, -1, -1,  0,  1,  2,  3,  4,  5],\n",
       "       [ 0,  1,  2, -1,  3,  4,  5, -1,  0,  1,  2,  3,  4,  5, -1, -1, -1]])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g.cell_face_as_dense()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, each column represent the cells of a face, and negative values signifies that the face is on the boundary. The cells are ordered so that the normal vector points from the cell in row 0 to row 1.\n",
    "\n",
    "Finally, we note that to get a cell-node relation, we can combine `cell_faces` and `face_nodes`. However, since `cell_faces` contains both positive and negative values, we need to take the absolute value of the data (without modifying `cell_faces` directly, since we may want to use the divergence operator later). This procedure is implemented in the method `cell_nodes()`, which returns a sparse matrix that can be handled in the usual way"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 4, 5], dtype=int32)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cn = g.cell_nodes()\n",
    "cn.indices[cn.indptr[0] : cn.indptr[1]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Simplex grids\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "PorePy has grid constructors for Cartesian grids in 2d and 3d, as well as simplex grids in 2d and 3d. The simplex grids can be specified either by point coordinates and a cell-node map (e.g. a Delaunay triangulation), or simply by the node coordinates. In the latter case, the Delaunay triangulation (or the 3d equivalent) will be used to construct the grid. As an example, we make a triangle grid using the nodes of g, distorting the y coordinate of the two central nodes slightly: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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333yTxo0b88UXXzB27Fhq1apFw4YNmTBhQo7tz8uVK8cXX3zBe++9R7Vq1fjss8/o169f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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fca362a3f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nodes = g.nodes[:2]\n",
    "nodes[1, 5:7] = np.array([1.2, 0.8])\n",
    "g = pp.TriangleGrid(nodes)\n",
    "g.compute_geometry()\n",
    "pp.plot_grid(g, figsize=(15,12))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A structured triangular grid (squares divided into two) is also provided:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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4HddtxeNxWrVqVbvTyczM5O677+a4446jurqaCy64gIEDB7J69WpmzpxJQUEB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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc9f9dbffd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = pp.StructuredTriangleGrid(np.array([3, 4]))\n",
    "g.compute_geometry()\n",
    "pp.plot_grid(g, figsize=(15,12))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import of grids from external meshing tools"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Currently, PorePy supports import of grids from Gmsh. This is mostly used for fractured domains (tutorial still to be made).\n",
    "\n",
    "The grid structure in PorePy is fairly general, and can support a much wider class of grids than those currently implemented. To import a new type of grid, all that is needed is to construct the face-node and cell-face maps, together with importing the node coordinates. Remaining geometric attributes can then be calculated by the `compute_geometry()` function. \n",
    "\n",
    "When implementing such a filter, note that the geometry computation tacitly *assumes an ordering of the nodes in each face*, in the sense that the edges of the faces are found by joining subsequent nodes in the face-node list. To illustrate the danger, consider the following example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cell volume 0.625\n"
     ]
    },
    {
     "data": {
      "image/png": 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Z2dkaGh2tSU6nal1i3yBJs51OPX3kiLp16KDFixcXRUTAr2VlZenuu+/WAw88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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc9f9dcb940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = pp.CartGrid([1, 1])\n",
    "\n",
    "# Move the second node so that the implicit edges are intersecting\n",
    "g.nodes[0, 1] = 0.5\n",
    "g.nodes[1, 1] = 2\n",
    "g.compute_geometry()\n",
    "# Print the cell volume, as computed\n",
    "print('Cell volume ' + str(g.cell_volumes[0]))\n",
    "# A short calculation will show that the actual cell volume is 1. \n",
    "\n",
    "pp.plot_grid(g, figsize=(15,12))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For more complex grids, errors in the node ordering may also lead to error messages / assertion errors in the geometry computation. \n",
    "\n",
    "For grid generation for fractured domains, see the tutorial `meshing_of_fractures`."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
